scipy 1.8.0 Pypi GitHub Homepage
Other Docs
NotesParametersReturnsBackRef
correlation_lags(in1_len, in2_len, mode='full')

Notes

Cross-correlation for continuous functions $f$ and $g$ is defined as:

$$\left ( f\star g \right )\left ( \tau \right ) \triangleq \int_{t_0}^{t_0 +T} \overline{f\left ( t \right )}g\left ( t+\tau \right )dt$$

Where $\tau$ is defined as the displacement, also known as the lag.

Cross correlation for discrete functions $f$ and $g$ is defined as:

$$\left ( f\star g \right )\left [ n \right ] \triangleq \sum_{-\infty}^{\infty} \overline{f\left [ m \right ]}g\left [ m+n \right ]$$

Where $n$ is the lag.

Parameters

in1_size : int

First input size.

in2_size : int

Second input size.

mode : str {'full', 'valid', 'same'}, optional

A string indicating the size of the output. See the documentation correlate for more information.

Returns

lags : array

Returns an array containing cross-correlation lag/displacement indices. Indices can be indexed with the np.argmax of the correlation to return the lag/displacement.

Calculates the lag / displacement indices array for 1D cross-correlation.

See Also

correlate

Compute the N-dimensional cross-correlation.

Examples

Cross-correlation of a signal with its time-delayed self.

>>> from scipy import signal
... from numpy.random import default_rng
... rng = default_rng()
... x = rng.standard_normal(1000)
... y = np.concatenate([rng.standard_normal(100), x])
... correlation = signal.correlate(x, y, mode="full")
... lags = signal.correlation_lags(x.size, y.size, mode="full")
... lag = lags[np.argmax(correlation)]
See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.signal._signaltools.correlation_lags scipy.signal._signaltools.correlate

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them


GitHub : /scipy/signal/_signaltools.py#289
type: <class 'function'>
Commit: